ArticleSensors (Basel, Switzerland)2021
High-Efficiency Super-Resolution FMCW Radar Algorithm Based on FFT Estimation.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Real-Time Radar Classification Based on Software-Defined Radio Platforms: Enhancing Processing Speed and Accuracy with Graphics Processing Unit Acceleration.Sensors (Basel, Switzerland) · 2024Article
- Research on a Super-Resolution and Low-Complexity Positioning Algorithm Using FMCW Radar Based on OMP and FFT in 2D Driving Scene.Sensors (Basel, Switzerland) · 2023Article
- Lightweight Super-Resolution with Self-Calibrated Convolution for Panoramic Videos.Sensors (Basel, Switzerland) · 2022Article
- Multi-Target Parameter Estimation of the FMCW-MIMO Radar Based on the Pseudo-Noise Resampling Method.Sensors (Basel, Switzerland) · 2022Article
- High-Resolution Doppler and Azimuth Estimation and Target Detection in HFSWR: Experimental Study.Sensors (Basel, Switzerland) · 2022Article
- A Low-Power High-Accuracy Urban Waterlogging Depth Sensor Based on Millimeter-Wave FMCW Radar.Sensors (Basel, Switzerland) · 2022Article
- FMCW Radar Estimation Algorithm with High Resolution and Low Complexity Based on Reduced Search Area.Sensors (Basel, Switzerland) · 2022Article
- Distributed Two-Dimensional MUSIC for Joint Range and Angle Estimation with Distributed FMCW MIMO Radars.Sensors (Basel, Switzerland) · 2021Article
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4 authors.
Funding
Abstract
This paper proposes a high-efficiency super-resolution frequency-modulated continuous-wave (FMCW) radar algorithm based on estimation by fast Fourier transform (FFT). In FMCW radar systems, the maximum number of samples is generally determined by the maximum detectable distance. However, targets are often closer than the maximum detectable distance. In this case, even if the number of samples is reduced, the ranges of targets can be estimated without degrading the performance. Based on this property, the proposed algorithm adaptively selects the number of samples used as input to the super-resolution algorithm depends on the coarsely estimated ranges of targets using the FFT. The proposed algorithm employs the reduced samples by the estimated distance by FFT as input to the super resolution algorithm instead of the maximum number of samples set by the maximum detectable distance. By doing so, the proposed algorithm achieves the similar performance of the conventional multiple signal classification algorithm (MUSIC), which is a representative of the super resolution algorithms while the performance does not degrade. Simulation results demonstrate the feasibility and performance improvement provided by the proposed algorithm; that is, the proposed algorithm achieves average complexity reduction of 88% compared to the conventional MUSIC algorithm while achieving its similar performance. Moreover, the improvement provided by the proposed algorithm was verified in practical conditions, as evidenced by our experimental results.
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